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Create app.py
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app.py
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import streamlit as st
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from bs4 import BeautifulSoup
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from langchain.embeddings import HuggingFaceEmbeddings
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import pickle
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import torch
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import io
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class CPU_Unpickler(pickle.Unpickler):
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def find_class(self, module, name):
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if module == 'torch.storage' and name == '_load_from_bytes':
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return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
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else: return super().find_class(module, name)
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@st.cache_resource
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def get_hugging_face_model():
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model_name = "mchochlov/codebert-base-cd-ft"
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hf = HuggingFaceEmbeddings(model_name=model_name)
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return hf
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@st.cache_resource
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def get_db():
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with open("codesearchdb.pickle", "rb") as f:
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db = CPU_Unpickler(f).load()
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return db
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def get_similar_links(query, db, embeddings):
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docs_and_scores = db.similarity_search_by_vector(embedding_vector)
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hrefs = []
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for docs in docs_and_scores:
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html_doc = docs.page_content
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soup = BeautifulSoup(html_doc, 'html.parser')
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href = [a['href'] for a in soup.find_all('a', href=True)]
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hrefs.append(href)
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return hrefs
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embedding_vector = get_hugging_face_model()
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db = get_db()
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st.title("📒 PDSASearch Engine 🤖 ")
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text_input = st.text_input("Enter some text")
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button = st.button("Find Similar Questions on Leetcode")
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if text_input:
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query = text_input
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answer = get_similar_links(query, db, embedding_vector)
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st.write("".join(answer))
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else:
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st.info("Please Input Valid Text")
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with st.sidebar:
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st.markdown("""
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### Created by Ashwin Rachha.
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Source Data : https://github.com/AshwinRachha/LeetCode-Solutions
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Medium Blog :
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""")
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